Short term and mid-range weather predictions (e.g., 30 minutes to several hours) of the location of organized storms and other weather features are extremely important to many sectors of the population. For example, aviation systems, traffic information systems, power companies and commuters realize important safety and economic benefits from accurate predictions of organized storms.
Cross-correlation image processing has been applied to weather radar images to generate short-term forecast images. For example, a method for predicting the motion of an organized storm is disclosed in U.S. Pat. No. 5,959,567, incorporated by reference herein. The method is based on applying an image filter matched to the structure of an organized storm to weather radar images at different times to thereby generate filtered weather radar images. An image tracker performs a cross-correlation of the filtered images to generate an array of track vectors that represents the movement of weather features, such as organized storms, in the time interval between the images. The track vectors are applied to a weather radar image to advect meteorological features and thereby generate a forecast image for a future time.
The method based on cross-correlation image processing does not have constraints imposed on the track vectors. In some instances, highly discontinuous track vector fields are produced that are contrary to a practical meteorological environment and therefore do not yield accurate forecast images. For example, track vectors at adjacent grid points in the array can cross over each other or point in opposite directions. The method of cross-correlation image processing also does not coalesce, nor probably acknowledge the uncertainty in each of the images.